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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Comics Recommendation Demo</span></li></ul></div><div 
id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div 
id="table-of-content-wrap
 per"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#tapster-demo-application">Tapster Demo Application</a> </li> <li> <a 
href="#apache-predictionio-setup">Apache PredictionIO Setup</a> </li> <li> <a 
href="#import-data">Import Data</a> </li> <li> <a 
href="#connect-demo-app-with-apache-predictionio">Connect Demo app with Apache 
PredictionIO</a> </li> <li> <a href="#links">Links</a> </li> <li> <a 
href="#conclusion">Conclusion</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/tapster.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Comics Recommendation 
Demo</span></li></ul></div><div id="page-title"><h
 1>Comics Recommendation Demo</h1></div></div><div class="content"> <h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In this demo, we 
will show you how to build a Tinder-style web application (named 
&quot;Tapster&quot;) recommending comics to users based on their likes/dislikes 
of episodes interactively.</p><p>The demo will use <a 
href="https://predictionio.apache.org/templates/similarproduct/quickstart/";>Similar
 Product Template</a>. Similar Product Template is a great choice if you want 
to make recommendations based on immediate user activities or for new users 
with limited history. It uses MLLib Alternating Least Squares (ALS) 
recommendation algorithm, a <a 
href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering";>Collaborative
 filtering</a> (CF) algorithm commonly used for recommender systems. These 
techniques aim to fill in the missing entries of a user-item association 
matrix. Users and products are described by a small set of latent factors
  that can be used to predict missing entries. A layman&#39;s interpretation of 
Collaborative Filtering is &quot;People who like this comic, also like these 
comics.&quot;</p><p>All the code and data is on GitHub at: <a 
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a>.</p><h3
 id='data' class='header-anchors'>Data</h3><p>The source of the data is from <a 
href="http://tapastic.com/";>Tapastic</a>. You can find the data files <a 
href="https://github.com/PredictionIO/Demo-Tapster/tree/master/data";>here</a>.</p><p>The
 data structure looks like this:</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/episode_list.csv";>Episode
 List</a> <code>data/episode_list.csv</code></p><p><strong>Fields:</strong> 
episodeId | episodeTitle | episodeCategories | episodeUrl | 
episodeImageUrls</p><p>1,000 rows. Each row represents one episode.</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/user_list.csv";>User
 Li
 ke Event List</a> 
<code>data/user_list.csv</code></p><p><strong>Fields:</strong> userId | 
episodeId | likedTimestamp</p><p>192,587 rows. Each row represents one user 
like for the given episode.</p><p>The tutorial has four major steps:</p> <ul> 
<li>Demo application setup</li> <li>PredictionIO installation and setup</li> 
<li>Import data into database and PredictionIO</li> <li>Integrate demo 
application with PredictionIO</li> </ul> <h2 id='tapster-demo-application' 
class='header-anchors'>Tapster Demo Application</h2><p>The demo application is 
built using Rails.</p><p>You can clone the existing application with:</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+<!DOCTYPE html><html><head><title>Comics Recommendation Demo</title><meta 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Comics Recommendation Demo</span></li></ul></div><div 
id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div 
id="table-of-content-wrapper
 "><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#tapster-demo-application">Tapster Demo Application</a> </li> <li> <a 
href="#apache-predictionio-setup">Apache PredictionIO Setup</a> </li> <li> <a 
href="#import-data">Import Data</a> </li> <li> <a 
href="#connect-demo-app-with-apache-predictionio">Connect Demo app with Apache 
PredictionIO</a> </li> <li> <a href="#links">Links</a> </li> <li> <a 
href="#conclusion">Conclusion</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/tapster.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Comics Recommendation 
Demo</span></li></ul></div><div id="page-title"><h1>C
 omics Recommendation Demo</h1></div></div><div class="content"> <h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In this demo, we 
will show you how to build a Tinder-style web application (named 
&quot;Tapster&quot;) recommending comics to users based on their likes/dislikes 
of episodes interactively.</p><p>The demo will use <a 
href="https://predictionio.apache.org/templates/similarproduct/quickstart/";>Similar
 Product Template</a>. Similar Product Template is a great choice if you want 
to make recommendations based on immediate user activities or for new users 
with limited history. It uses MLLib Alternating Least Squares (ALS) 
recommendation algorithm, a <a 
href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering";>Collaborative
 filtering</a> (CF) algorithm commonly used for recommender systems. These 
techniques aim to fill in the missing entries of a user-item association 
matrix. Users and products are described by a small set of latent factors th
 at can be used to predict missing entries. A layman&#39;s interpretation of 
Collaborative Filtering is &quot;People who like this comic, also like these 
comics.&quot;</p><p>All the code and data is on GitHub at: <a 
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a>.</p><h3
 id='data' class='header-anchors'>Data</h3><p>The source of the data is from <a 
href="http://tapastic.com/";>Tapastic</a>. You can find the data files <a 
href="https://github.com/PredictionIO/Demo-Tapster/tree/master/data";>here</a>.</p><p>The
 data structure looks like this:</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/episode_list.csv";>Episode
 List</a> <code>data/episode_list.csv</code></p><p><strong>Fields:</strong> 
episodeId | episodeTitle | episodeCategories | episodeUrl | 
episodeImageUrls</p><p>1,000 rows. Each row represents one episode.</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/user_list.csv";>User
 Like 
 Event List</a> <code>data/user_list.csv</code></p><p><strong>Fields:</strong> 
userId | episodeId | likedTimestamp</p><p>192,587 rows. Each row represents one 
user like for the given episode.</p><p>The tutorial has four major steps:</p> 
<ul> <li>Demo application setup</li> <li>PredictionIO installation and 
setup</li> <li>Import data into database and PredictionIO</li> <li>Integrate 
demo application with PredictionIO</li> </ul> <h2 id='tapster-demo-application' 
class='header-anchors'>Tapster Demo Application</h2><p>The demo application is 
built using Rails.</p><p>You can clone the existing application with:</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3</pre></td><td class="code"><pre><span class="gp">$ </span>git clone  
https://github.com/PredictionIO/Demo-Tapster.git
 <span class="gp">$ </span><span class="nb">cd </span>Demo-Tapster

http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/38bcf03c/demo/textclassification/index.html
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class="final" href="/datacollection/"><span>Event Server 
Overview</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventapi/"><span>Collecting Data with 
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href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
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href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
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href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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class="final" href="/evaluation/"><span>Overview</span></a></li><li 
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class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metri
 cchoose/"><span>Choosing Evaluation Metrics</span></a></li><li 
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class="level-3"><a class="final" 
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Serving</span></a></li><li class="level-3"><a class="final" 
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href="/templates/recommen
 dation/batch-evaluator/"><span>Batch Persistable 
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Start</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
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class="final" href="/templates/similarproduct/quickstart/"><span>Quick Sta
 rt</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
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Properties</span></a></li><li class="level-3"><a class="final" 
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for Users</span></a></li><li class="level-3"><a class="final" 
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Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classifi
 cation</span></a><ul><li class="level-3"><a class="final" 
href="/templates/classification/quickstart/"><span>Quick 
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href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="
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class="level-2"><a class="final active" 
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Cadence</span></a></li><li class="level-2"><a class="final" 
href="/resources/intellij/"><span>Developing Engines with IntelliJ 
IDEA</span></a></li><li class="level-2"><a class="final" 
href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li 
class="level-2"><a class="final" 
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class="level-1"><a class=
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classification Engine
  Tutorial</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-deployment">Engine
  Deployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/textclassification.html.md.erb";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gague the general sentiment in a geographical 
area by analyzing T
 witter data; and news articles can be automatically categorized based solely 
on the text content.There are a wide array of machine learning models you can 
use to create, or train, a predictive model to assign an incoming article, or 
query, to an existing category. Before you can use these techniques you must 
first transform the text data (in this case the set of news articles) into 
numeric vectors, or feature vectors, that can be used to train your 
model.</p><p>The purpose of this tutorial is to illustrate how you can go about 
doing this using PredictionIO&#39;s platform. The advantages of using this 
platform include: a dynamic engine that responds to queries in real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE architecture allowing 
Data
  Scientists to focus on tasks related to modeling. This tutorial will 
exemplify some of these ideas by guiding you through PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>git clone https
 ://github.com/apache/predictionio-template-text-classifier.git &lt; Your new 
engine directory &gt;
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class="level-1"><a class="expandible" href="#"><span>Getting 
Started</span></a><ul><li class="level-2"><a class="final" 
href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a 
class="final" href="/install/"><span>Installing Apache 
PredictionIO</span></a></li><li class="level-2"><a class="final" 
href="/start/download/"><span>Downloading an Engine Template</span></a></li><li 
class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your 
First Engine</span></a></li><li class="level-2"><a class="final" 
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Engine</span></a></li><li class="level-2"><a class="final" 
href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li 
class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying 
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href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Customizing an 
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href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a 
class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li 
class="level-2"><a class="final" 
href="/customize/troubleshooting/"><span>Troubleshooting Engine 
Development</span></a></li><li class="level-2"><a class="final" 
href="/api/current/#package"><span>Engine Scala 
APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" href="
 #"><span>Collecting and Analyzing Data</span></a><ul><li class="level-2"><a 
class="final" href="/datacollection/"><span>Event Server 
Overview</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventapi/"><span>Collecting Data with 
REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
Webhooks</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
Batch</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/analytics/"><span>Using Analytics 
Tools</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/plugin/"><span>Event Server 
Plugin</span></a></li></ul></li><li class="level-1"><a class
 ="expandible" href="#"><span>Choosing an Algorithm</span></a><ul><li 
class="level-2"><a class="final" href="/algorithm/"><span>Built-in Algorithm 
Libraries</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/switch/"><span>Switching to Another 
Algorithm</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Tuning and Evaluation</span></a><ul><li class="level-2"><a 
class="final" href="/evaluation/"><span>Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricch
 oose/"><span>Choosing Evaluation Metrics</span></a></li><li class="level-2"><a 
class="final" href="/evaluation/metricbuild/"><span>Building Evaluation 
Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>System Architecture</span></a><ul><li class="level-2"><a 
class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>PredictionIO® Official 
Templates</span></a><ul><li class="level-2"><a class="final" 
href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
class="expandible" href="#"><span>Recommendation</span></a><ul><li 
class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li class
 ="level-3"><a class="final" 
href="/templates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classification Engine Tu
 torial</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-deployment">Engine De
 ployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/textclassification.html.md.erb";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gague the general sentiment in a geographical 
area by analyzing Twit
 ter data; and news articles can be automatically categorized based solely on 
the text content.There are a wide array of machine learning models you can use 
to create, or train, a predictive model to assign an incoming article, or 
query, to an existing category. Before you can use these techniques you must 
first transform the text data (in this case the set of news articles) into 
numeric vectors, or feature vectors, that can be used to train your 
model.</p><p>The purpose of this tutorial is to illustrate how you can go about 
doing this using PredictionIO&#39;s platform. The advantages of using this 
platform include: a dynamic engine that responds to queries in real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE architecture allowing 
Data Sc
 ientists to focus on tasks related to modeling. This tutorial will exemplify 
some of these ideas by guiding you through PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>git clone https://
 github.com/apache/predictionio-template-text-classifier.git &lt; Your new 
engine directory &gt;
 </pre></td></tr></tbody></table> </div> <h2 id='engine-overview' 
class='header-anchors'>Engine Overview</h2><p>The engine follows the DASE 
architecture which we briefly review here. As a user, you are tasked with 
collecting data for your web or application, and importing it into 
PredictionIO&#39;s Event Server. Once the data is in the server, it can be read 
and processed by the engine via the Data Source and Preparation components, 
respectively. The Algorithm component uses the processed, or prepared, data to 
train a set of predictive models. Once you have trained these models, you are 
ready to deploy your engine and respond to real-time queries via the Serving 
component which combines the results from different fitted models. The 
Evaluation component is used to compute an appropriate metric to test the 
performance of a fitted model, as well as aid in the tuning of model hyper 
parameters.</p><p>This engine template is meant to handle text classification 
which means you will be worki
 ng with text data. This means that a query, or newly observed documents, will 
be of the form:</p><p><code>{text : String}</code>.</p><p>In the running 
example, a query would be an incoming news article. Once the engine is deployed 
it can process the query, and then return a Predicted Result of the 
form</p><p><code>{category : String, confidence : Double}</code>.</p><p>Here 
category is the model&#39;s class assignment for this new text document (i.e. 
the best guess for this article&#39;s categorization), and confidence, a value 
between 0 and 1 representing your confidence in the category prediction (0 
meaning you have no confidence in the prediction). The Actual Result is of the 
form</p><p><code>{category : String}</code>.</p><p>This is used in the 
evaluation stage when estimating the performance of your predictive model (how 
well does the model predict categories). Please refer to the <a 
href="https://predictionio.apache.org/customize/";>following tutorial</a> for a 
more detailed exp
 lanation of how your engine will interact with your web application, as well 
as an in depth-overview of DASE.</p><h2 id='quick-start' 
class='header-anchors'>Quick Start</h2><p>This is a quick start guide in case 
you want to start using the engine right away. Sample email data for spam 
classification will be used. For more detailed information, read the subsequent 
sections.</p><h3 id='1.-create-a-new-application.' class='header-anchors'>1. 
Create a new application.</h3><p>After the application is created, you will be 
given an access key and application ID for the application.</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ </span>pio app new MyTextApp
 </pre></td></tr></tbody></table> </div> <h3 id='2.-import-the-tutorial-data.' 
class='header-anchors'>2. Import the tutorial data.</h3><p>There are three 
different data sets available, each giving a different use case for this 
engine. Please refer to the <strong>Data Source: Reading Event Data</strong> 
section to see how to appropriate modify the <code>DataSource</code> class for 
use with each respective data set. The default data set is an e-mail spam data 
set.</p><p>These data sets have already been processed and are ready for <a 
href="/datacollection/batchimport/">batch import</a>. Replace <code>***</code> 
with your actual application ID.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
 2

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